Internet of things (IoT) enabled wireless sensor system enabling process control, predictive maintenance of electrical distribution networks, liquid and gas pipelines and monitoring of air pollutants including nuclear, chemical, and biological agents using attached and/or embedded passive electromagnetic sensors

A method of computing a phenomena value from a modified acoustic wave, comprising steps of:

• a. a first electromagnetic controller communicator of a plurality of electromagnetic controller communicators emitting an electromagnetic wave where said electromagnetic wave is captured by an inbound interdigital transducer located on a passive electromagnetic sensor;
• b. wherein said inbound interdigital transducer converts said electromagnetic wave to an acoustic wave, which travels along a propagation path;
• c. wherein said acoustic wave passes through a test delay gap and is modified based on a presence of a phenomena to create the modified acoustic wave;
• d. wherein said modified acoustic wave is analyzed by an onboard microprocessor of said passive electromagnetic sensor, where said microprocessor computes the phenomena value from said modified acoustic wave;
• e. transmitting said phenomena value to a network, wherein the network includes:
• a plurality of passive electromagnetic sensors;
• a plurality of electromagnetic controller communicators;
• at least one user computing resource or at least one user computer;
• and at least one supercomputer with artificial intelligence means, whereby at least one of the plurality of electromagnetic controller communicators of whereby said network processes said phenomena value and determines whether said phenomena value is anomalous by comparison of said phenomena value with a known relational module wherein the artificial intelligence means is configured to learn phenomena patterns representing normal and anomalous conditions at least based on previous phenomena measurement values measured by the plurality of passive electromagnetic sensors and to generate the relational module for determining an anomalous condition wherein said network issues execution commands to any segment of a utility grid;
• and
• f. whereby programming logic of said network enables process control and predictive maintenance commands based on said comparison of said phenomena value with said known relational module to maintain wellness of said utility grid.

A method of utilizing modified acoustic waves to enable process control and predictive maintenance comprising steps of:

• a. harvesting of a first electromagnetic wave by a passive electromagnetic sensor;
• b. converting said electromagnetic wave to an acoustic wave;
• c. modifying said acoustic wave based on a phenomena to be measured to create a first modified acoustic wave;
• d. transmitting said first modified acoustic wave to a microprocessor of said passive electromagnetic sensor or an electromagnetic controller communicator;
• e. whereby said microprocessor or electromagnetic controller communicator computes a first value for said phenomena to be measured from said first modified acoustic wave;
• f. transmitting said first value to a network, wherein the network includes:
• a plurality of passive electromagnetic sensors;
• a plurality of electromagnetic controller communicators;
• at least one user computing resource or at least one user computer;
• and at least one supercomputer with artificial intelligence means, whereby at least one of the electromagnetic controller communicators of whereby said network processes said first value and determines whether said value is anomalous by comparison of said first value with a known relational module wherein the artificial intelligence means is configured to learn phenomena patterns representing normal and anomalous conditions at least based on phenomena measurement values measured by the plurality of passive electromagnetic sensors and to generate the relational module for determining a first anomalous condition wherein said network issues execution commands to any segment of a utility grid;
• and
• g. whereby programming logic of said network enables process control and predictive maintenance commands based on said comparison of said first value with said known relational module to maintain wellness of said utility grid.